Rapid identification of candidate genes for resistance to ...203.64.245.61/full_text/e13786.pdf · control (cv. Castlerock), were sown in 209 cell seedling trays with peat moss–vermiculi

RESEARCH ARTICLE

Rapid identification of candidate genes for

resistance to tomato late blight disease using

next-generation sequencing technologies

Ramadan A. Arafa1¤a, Mohamed T. Rakha2¤b, Nour Elden K. Soliman3☯, Olfat M. Moussa3☯,

Said M. Kamel1, Kenta Shirasawa4*

1 Plant Pathology Research Institute, Agricultural Research Center, Giza, Egypt, 2 Department of

Horticulture, Faculty of Agriculture, University of Kafrelsheikh, Kafr El-Sheikh, Egypt, 3 Department of Plant

Pathology, Faculty of Agriculture, Cairo University, Giza, Egypt, 4 Department of Frontier Science, Kazusa

DNA Research Institute, Chiba, Japan

☯ These authors contributed equally to this work.

¤a Current address: Department of Frontier Science, Kazusa DNA Research Institute, Chiba, Japan

¤b Current address: World Vegetable Center, Shanhua, Tainan, Taiwan

* [email protected]

Abstract

Tomato late blight caused by Phytophthora infestans (Mont.) de Bary, also known as the

Irish famine pathogen, is one of the most destructive plant diseases. Wild relatives of tomato

possess useful resistance genes against this disease, and could therefore be used in breed-

ing to improve cultivated varieties. In the genome of a wild relative of tomato, Solanum hab-

rochaites accession LA1777, we identified a new quantitative trait locus for resistance

against blight caused by an aggressive Egyptian isolate of P. infestans. Using double-digest

restriction site–associated DNA sequencing (ddRAD-Seq) technology, we determined

6,514 genome-wide SNP genotypes of an F2 population derived from an interspecific cross.

Subsequent association analysis of genotypes and phenotypes of the mapping population

revealed that a 6.8 Mb genome region on chromosome 6 was a candidate locus for disease

resistance. Whole-genome resequencing analysis revealed that 298 genes in this region

potentially had functional differences between the parental lines. Among of them, two genes

with missense mutations, Solyc06g071810.1 and Solyc06g083640.3, were considered to

be potential candidates for disease resistance. SNP and SSR markers linking to this region

can be used in marker-assisted selection in future breeding programs for late blight disease,

including introgression of new genetic loci from wild species. In addition, the approach

developed in this study provides a model for identification of other genes for attractive agro-

nomical traits.

Introduction

Plants suffer from many biotic and abiotic stresses [1], which reduce quantity and quality of

crop production worldwide. Late blight disease is caused by the hemibiotrophic oomycete Phy-tophthora infestans (Mont.) de Bary, one of the most destructive plant pathogens. Phytophthora

PLOS ONE | https://doi.org/10.1371/journal.pone.0189951 December 18, 2017 1 / 15

a1111111111

a1111111111

a1111111111

a1111111111

a1111111111

OPENACCESS

Citation: Arafa RA, Rakha MT, Soliman NEK,

Moussa OM, Kamel SM, Shirasawa K (2017) Rapid

identification of candidate genes for resistance to

tomato late blight disease using next-generation

sequencing technologies. PLoS ONE 12(12):

e0189951. https://doi.org/10.1371/journal.

pone.0189951

Editor: Mark Gijzen, Agriculture and Agri-Food

Canada, CANADA

Received: September 11, 2017

Accepted: December 5, 2017

Published: December 18, 2017

Copyright: © 2017 Arafa et al. This is an open

access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: Nucleotide sequence

data for the ddRAD-Seq and WGRS analyses are

available in the DDBJ Sequence Read Archive

under accession numbers DRA005972 and

DRA005973.

Funding: The study was financially supported by

The Ministry of Higher Education and Scientific

Research (MHESR), Egypt (Grant# 2013/2014-

547), and the Kazusa DNA Research Institute

Foundation, Japan. The funders had no role in

https://doi.org/10.1371/journal.pone.0189951

http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0189951&domain=pdf&date_stamp=2017-12-18








http://creativecommons.org/licenses/by/4.0/

infestans is well known as the causative agent of the Great Famine in Ireland between 1845 and

1852, which devastated potato production (Solanum tuberosum) [2]. After potato, tomato (S.

lycopersicum L.) is the second most agriculturally important crop in the Solanaceae family. The

annual global productivity of tomato has increased dramatically, to 170 million tons in 2014

[3]. However, tomato can also be damaged by the late blight disease, particularly in cool tem-

peratures, high relative humidity (RH), and rainy or foggy conditions [4], resulting in 100%

economic losses in open fields and greenhouses.

Tomato has been used in molecular genetic and genomic studies as a model for fruiting

plants [5] because of its compact genome (~950 Mb) and the simple diploid genome composi-

tion of family Solanaceae. The genome sequence of tomato [6] has enabled discovery of

genome-wide single-nucleotide polymorphisms (SNPs) and development of advanced molec-

ular markers [7–10]. Although the genetic diversity of the cultivated tomato is limited [11], its

wild relatives S. pennellii, S. habrochaites, S. peruvianum, and S. pimpinellifolium have many

useful traits potentially applicable to improvement of the agricultural varieties. Therefore,

introduction of wild tomato species into tomato breeding programs could facilitate develop-

ment of new tomato lines [12–15]. Indeed, five race-specific resistance (R) genes that confer

various levels of resistances against P. infestans isolates Ph-1, Ph-2, Ph-3, Ph-4, and Ph-5 have

been identified [16–22] and applied to molecular breeding by marker-assisted selection (MAS)

[20]. However, a serious problem in breeding by interspecific crossing is linkage drag, in

which undesirable traits linked to target traits in the wild relatives are introgressed in elite cul-

tivars [23, 24].

In the genomics era, advanced molecular markers and genotyping technologies have helped

to solve this problem [25, 26]. Simple sequence repeat (SSR) markers are useful for genomics

and breeding in tomato [27–29]; however, analysis of large numbers of genome-wide SSR

markers across multiple samples, such as breeding materials, is time-consuming and laborious.

However, next-generation sequencing (NGS) technologies, including high-throughput

sequencing and sophisticated bioinformatics techniques, can overcome these limitations.

Restriction site–associated DNA sequencing (RAD-Seq) [30–32] and an alternative technique,

double-digest RAD-Seq (ddRAD-Seq) [33], can skim through the genome with low cost and

high throughput. These methods can be successfully implemented in gene mapping, including

quantitative trait locus (QTL) analysis and genome-wide association studies (GWAS), of a vast

array of crops [32, 34–38]. On the other hand, whole-genome resequencing (WGRS) enables

prediction of the effects of sequence variants on gene function throughout the genome [39–

43]. Therefore, a combination of RAD-Seq and WGRS analysis represents a powerful strategy

for rapidly identifying candidate genes responsible for traits of interests.

Development of new tomato lines with resistance to late blight disease would be a straight-

forward, effective, and environmentally safe approach to managing late blight disease. There-

fore, in this study, we aimed to identify map positions of genetic loci derived from a wild

tomato relative, S. habrochaites that control resistance to late blight disease caused by P. infes-tans. We applied a ddRAD-Seq pipeline that we developed in a previous study [33] to genetic

mapping of the resistance loci, and then we used a WGRS strategy to predict candidate genes

for late blight disease resistance.

Materials and methods

Plant materials

A cultivated tomato (S. lycopersicum), Castlerock, and its wild relative, S. habrochaites(LA1777), were used in this study. Castlerock was chosen because it is susceptible to late blight

disease, and LA1777 was selected because it is resistant to the Egyptian P. infestans population,

NGS-based rapid identification of resistance genes to tomato late blight


study design, data collection and analysis, decision

to publish, or preparation of the manuscript.

Competing interests: The authors have declared

that no competing interests exist.


as shown in a previous study by our group [15]. Seeds of Castlerock and LA1777 were pro-

vided by the Horticulture Research Institute, Agricultural Research Center (ARC), Egypt, and

the Tomato Genetic Research Center (TGRC), Davis, CA, USA, respectively. An F2 population

(n = 344) was generated from an interspecific cross between Castlerock and LA1777.

Isolation and purification of P. infestans isolate

Isolation of the P. infestans population was conducted by placing host infected tissues under

organic potato slices in converted Petri dishes containing water agar and incubating at 18˚C

for 7–10 days. Sporangia were picked from the abundant sporulation on the top of the slices

and transferred directly onto the recommended media. Rye sucrose agar (RSA) medium [44]

(60 g of rye grains, 20 g of sucrose, and 20 g of agar per liter) was used for isolation, growth,

and maintenance of P. infestans isolates. Pure culture of P. infestans was conducted on rye

slants at 18˚C, and the cultures were preserved as a stock for further studies. P. infestans isolate

EG_12 was selected from the stock of the Plant Pathology Research Institute, ARC, which was

overcome five tomato genotypes containing R genes (Ph-1, Ph-2, and Ph-3) as well as Super

Strain B, a susceptible tomato cultivar control based on virulence test [15].

Inoculum preparation and late blight assessment

Seeds of F2 progeny and the parental lines Castlerock and LA1777, as well as the susceptible

control (cv. Castlerock), were sown in 209 cell seedling trays with peat moss–vermiculite mix-

ture (1:1 volume) in a greenhouse (25 ± 2˚C, 16/8 h day/night). Plants were watered and fertil-

ized regularly with N:P:K 19:19:19, and all traditional agricultural transactions were applied to

maintain the plants under appropriate and healthy conditions. Eight weeks after sowing, all

trays were moved from the greenhouse to growth room at the Plant Pathology Research Insti-

tute, ARC, for artificial inoculation with P. infestans EG_12 and late blight assessment.

Inoculum preparation of isolate EG_12 was performed as described [15]. Prior to artificial

inoculation, the suspension was chilled at 4˚C for 2–4 h [45] to allow cleavage of sporangia

and release of zoospores.

After inoculum preparation, the conditions in the growth room were adjusted to 20±2˚C

and 100% RH for 48 h in darkness, followed by 20˚C, up to 90% RH [46], and 10/14 h day/

night for 10 days. All tested plants were hand-sprayed with an atomizer to cover all parts of the

foliage and kept in a growth room under the conditions described above. The plants were

wrapped with a plastic sheet to keep RH above 90%. F2 plants were evaluated individually for

late blight disease at 10 days post inoculation (dpi) by visually scoring disease severity accord-

ing to a numerical rating (0–6) as described [47] with some modifications: 0, immune; 1,

highly resistant; 2, resistant; 3, moderately resistant; 4, moderately susceptible; 5, susceptible; 6,

highly (91–100%) susceptible. All inoculated plants were scored when the susceptible control

exhibited 100% disease severity (complete death).

DNA extraction and sequencing analysis

Total genomic DNA was extracted from young leaves of the two parents and the F2 progeny

using the DNeasy Plant Mini Kit (Qiagen Inc., Hilden, Germany). Genotypes were analyzed

using ddRAD-Seq technology with the restriction enzymes PstI and MspI (S1 Table). The

ddRAD-Seq libraries were constructed and sequenced on a HiSeq 2000 platform (Illumina,

San Diego, CA, USA) in paired-end 93 bp mode as described [33].

The two parents were further subjected to WGRS. Paired-end sequencing libraries with an

insert size of 600 bp were prepared as described [48]. The nucleotide sequences were




determined using massively parallel sequencing by synthesis on an Illumina HiSeq2000 (Illu-

mina) in paired-end 93 bp mode.

Computational data processing and association analysis

Primary data processing of ddRAD-Seq and WGS sequence reads was performed as described

in our previous studies [33, 49] with some modifications. Low-quality sequences were

removed and adapters were trimmed using PRINSEQ (version 0.20.4) [50] and fastx_clipper

in the FASTX-Toolkit (version 0.0.13) (http://hannonlab.cshl.edu/fastx_toolkit). The filtered

reads were mapped onto tomato genome SL3.0 [6], used as a reference sequence, with Bowtie

2 (version 2.1.0; parameters:—minins 100—no-mixed) [51]. The resultant sequence align-

ment/map format (SAM) files were converted to binary sequence alignment/map format

(BAM) files and subjected to SNP calling using the mpileup option of SAMtools (version

0.1.19; parameters: default) [52] to yield a variant call format (VCF) file including SNP infor-

mation. Moreover, to obtain high-confidence SNP markers, VCF files were filtered with

VCFtools (version 0.1.14) [53]. The parameters for VCFtools were as follows:—maf 0.05—

max-alleles 2—min-alleles 2—minDP 10—minQ 10—non-ref-ac 2—max-non-ref-ac 2—max-

missing 0.75 for WGRS data; and—remove-indels—minDP 5—minQ 20—max-missing 1—

min-alleles 2—max-alleles 2 for ddRAD-Seq data. Annotations of SNP effects on gene func-

tions were predicted using SnpEff (version 4.2) [54]. The association analysis between pheno-

type and genotype data was performed using the generalized linear model (GLM) of trait

analysis by association, evolution, and linkage (TASSEL) version 5.2.33 [55].

SSR marker analysis

A total of 13 expressed sequence tag (EST)-derived SSR markers (TES markers) and ten

genome-derived SSR markers (TGS markers) (S2 Table) were selected from the candidate

genome regions on chromosome 6 for late blight resistance, as described in the Kazusa Marker

Database (http://marker.kazusa.or.jp) [29]. These markers were used for polymorphic

analysis.

Data availability

Nucleotide sequence data for the ddRAD-Seq and WGRS analyses are available in the DDBJ

Sequence Read Archive under accession numbers DRA005972 and DRA005973.

Results

Phenotypic assessment of disease response for the F2 population

To identify QTLs associated with late blight resistance, an F2 mapping population of 383

plants, as well as the susceptible and resistant parents, were infected with an Egyptian isolate of

P. infestans EG_12, and disease severity was evaluated on a numerical scale (0–6). All tested

materials were individually scored 10 days after artificial inoculation, when the susceptible

control plants reached the highest score of disease severity. The evaluated population was

divided into seven categories based on the scale. The F2 population exhibited broad variations

in reaction to the pathogen, ranging from complete resistant (0) to highly susceptible (6). In

addition, varying degrees of disease severity were detected in all tested plants. Among the F2

population, a disease severity score of 4 was most prevalent (79 plants, 22.97%), followed by

score of 6 (76 plants, 22.09%). On the other hand, a score of 1 (highly resistant) was least preva-

lent (26 plants, 7.56%) (Fig 1). Also, the whole-plant assay under environmentally controlled

conditions confirmed that the parent S. habrochaites accession LA1777 was resistant, whereas



http://hannonlab.cshl.edu/fastx_toolkit

http://marker.kazusa.or.jp/


the cultivated tomato cv. Castlerock was highly susceptible, with severe late blight symptoms

(completely blighted, 100%) (Fig 2). Therefore, the tomato wild accession LA1777 should be

considered a genetic resource for identification of QTLs associated with late blight resistance.

Association analysis with SNPs based on ddRAD-Seq

In the ddRAD-Seq analysis of the parental lines and a subset of the F2 population (n = 150),

a mean of 616,763 reads was obtained for each sample. The total numbers of high-quality

paired reads of the parental lines, cv. Castlerock and S. habrochaites accession LA1777, were

1,010,157 and 367,193, respectively (S3 Table). The read numbers obtained in this study is

enough for the following linkage analysis [33]. The alignment rate to the reference tomato

genome build SL3.0 was approximately 90.0% in the F2 population, whereas those of the two

parents were 93.4% (Castlerock) and 88.99% (LA1777). From the alignment data, 11,348 SNP

candidates were obtained, of which 6,514 were selected as a high-quality data set (S4 Table)

based on criteria described in Materials and Methods. The mean number of SNPs per

Fig 1. Disease severity rating 0–6 of F2 mapping population (n = 344) of the cross cv. Castlerock (S.

lycopersicum) x S. habrochaites accession LA1777 to aggressive Egyptian isolate of P. infestans.

https://doi.org/10.1371/journal.pone.0189951.g001

Fig 2. Screening the parental lines for resistance to P. infestans isolate EG_12 using whole-plant

assay under controlled conditions. (A) Highly susceptible parent cv. Castlerock, (B) highly resistant parent

S. habrochaites accession LA1777.







chromosome (excluding 17 SNPs on sequences unassigned to the tomato chromosomes) was

543, with a variant rate of one SNP every 123,921 bases, ranging from 354 SNPs on chromo-

some 9 (1 SNP/205,950 bp) to 780 on chromosome 2 (1 SNP/71,766 bp). The SNPs comprised

3,406 downstream gene variants following 3,374 intron variants and 2,371 upstream gene vari-

ants. The physical positions of the 6,514 SNPs were distributed over all 12 chromosomes (Fig 3

and S1 Fig), but the distribution patterns were highly biased: most of the SNPs were located at

both ends of each chromosome, which are gene-rich euchromatic regions; an exception to this

pattern is chromosome 2, which has repetitive rDNA sequences at the top of the chromosome.

To detect genetic loci for resistance to P. infestans isolate EG_12, GWAS were performed

with 6,514 high-confidence SNPs from the ddRAD-Seq and phenotypic data. Based on GLM

with false discovery rate (FDR) of 0.1 [56], 124 SNPs on a 6.8 Mb region of chromosome 6

(42,859,404 bp to 49,665,578 bp), including 665 predicted genes, were significantly associated

with phenotypic variation. Among those, the SNP at 48,363,490 bp on chromosome 6 exhib-

ited the highest association with late blight disease resistance (Fig 4).

Fig 3. Representation of high-confidence single nucleotide polymorphism (SNP) markers along

chromosome 6 of tomato mapped on SL3.0 version of the tomato reference genome. Candidate

genomic region tightly related to plant disease resistance was predicted on ch06 based on SnpEff annotation,

(A) the double-digest restriction site–associated DNA sequencing (ddRAD-Seq), and (B) the whole-genome

shotgun resequencing (WGRS) technologies. The remaining chromosomes ch00 –ch12 are shown in S1 Fig.


Fig 4. Manhattan plots for genome-wide association studies of generalized linear model (GLM)

analysis of late blight disease resistance using TASSEL software. The SNP markers were generated

using NGS technology, double-digest restriction site–associated DNA sequencing (ddRAD-Seq).







Validation of the associated loci by SSR marker analysis

To validate the results of the association studies, we subjected the remaining F2 lines (n = 194)

not analyzed with the ddRAD-Seq to genotyping analysis with 23 SSR markers that were physi-

cally and genetically close to the candidate region (S2 Table). Out of the 23 SSRs, 5 markers

(TES0422, TES0014, TES1344, TES0945, and TES0213) exhibited polymorphism between the

parental lines, Castlerock and LA1777 (Table 1). Therefore, we analyzed the genotypes of the

additional 194 lines, as well as the 150 lines used for ddRAD-Seq, using the five selected SSR

markers. As expected, the phenotypes of F2 lines with homozygous alleles from LA1777 or

Castlerock differed significantly (resistant in the case of LA1777 alleles, and susceptible in the

case of Castlerock alleles), even though severe segregation distortion that is often reported in

intercrossing populations [29] and references therein was observed in this locus. This addi-

tional SSR analysis confirmed the results of the GWAS using ddRAD-Seq technology.

Whole-genome shotgun resequencing

To identify sequence variations in the candidate genetic locus, we performed WGRS analysis

on the parents. Totals of 174.9 and 189.9 million high-quality reads (17-18x genome coverage)

for Castlerock and LA1777, respectively, were obtained and mapped onto the reference

genome sequence, with alignment rates of 96.9% for Castlerock and 70.7% for LA1777 (S5

Table).

Across the genome including “chromosome 0”, genome sequences not assigned to any

chromosomes, we identified a total of 4,180,666 high-quality sequence variations (one

sequence variation every 198 bp), including 4,022,951 SNPs and 157,715 indels. The ratio of

transitions/transversions (Ts/Tv) was calculated to be 1.08. The SNPs were positioned on all

tomato chromosomes without large gaps (Fig 3 and S1 Fig), as observed for the genome posi-

tions of SNPs detected by ddRAD-Seq. Among the 4,180,666 sites, 14,755 (0.27%) sequence

variations in 2,557 genes were predicted by the SnpEff software to possess high-impact (e.g.,

nonsense or frame-shift mutations) on gene functions, whereas 57,390 (1.038%) polymor-

phisms in 15,934 genes were predicted to have moderate impacts (e.g., missense mutations)

(S6 Table).

On the other hand, in the 6.8 Mb candidate locus on chromosome 6, we identified 8,367

polymorphic sites (7,684 SNPs and 683 indels) at 1 variation/814 bases with a Ts/Tv ratio of

1.36. Among the 8,367 sites, 168 (0.87%) sequence variations in 24 genes were predicted to

have high impacts, and 516 (2.67%) polymorphisms in 274 genes were predicted to have mod-

erate impacts. In the candidate regions, the ratio of high-impact variations versus moderate-

impact variations was 3-fold higher than in the genome overall, whereas variation density was

lower. Among them, two genes located in the interval between the significant SNPs were con-

sidered as potential candidates for blight disease resistance genes. One was Solyc06g071810.1

encoding the leucine-rich repeat (LRR) receptor–like serine/threonine-protein kinase FEI 1

having a missense mutation at the 39th position (Asp in Castlerock, Glu in LA1777), while the

other was Solyc06g083640.3 for a LRR family protein with a missense mutation at the 111th

position (Gln in Castlerock and Lys in LA1777).

Discussion

In this study, we identified a resistance locus for late blight disease on chromosome 6 of

tomato. This locus is at a different genome position than previously reported resistance loci

[22, 57, 58], and should therefore be considered novel. The result of GWAS was validated by

the SSR analysis of the additional F2 lines (Table 1). In general, to confirm the accuracy of the

genetic analysis of GWAS and QTL analysis, the results are validated by genotyping with DNA




Table 1. Genotyping of F2 mapping population with five EST-SSR markers.

SSR marker Chromosome Position (bp)1 Scale2 Allele Amplified samples Total tested samples

LA1777 Castlerock Hete.

TES0422 SL3.0ch06 44975890 0 17 0 13 317 344

1 12 0 11

2 20 0 18

3 14 0 33

4 26 2 47

5 7 1 22

6 20 8 46

Mean3 3.0431b 5.5455a 3.7895c

TES0014 SL3.0ch06 45297826 0 23 0 12 343 344

1 15 0 11

2 27 1 17

3 19 0 33

4 31 0 48

5 7 1 23

6 22 10 43

Mean3 2.8958b 5.5833a 3.7914c

TES1344 SL3.0ch06 45438555 0 23 0 12 340 344

1 14 1 11

2 27 1 17

3 18 1 32

4 30 0 46

5 7 1 23

6 24 9 43

Mean3 2.9441b 5.0000a 3.7935c

TES0945 SL3.0ch06 47342901 0 25 1 7 328 344

1 15 0 11

2 26 1 16

3 17 2 32

4 34 0 41

5 7 0 22

6 23 8 40

Mean3 2.9048b 4.6667a 3.8639a

TES0213 SL3.0ch06 49713763 0 24 2 9 343 344

1 17 0 9

2 23 1 21

3 20 1 31

4 35 0 44

5 10 2 19

6 23 8 44

Mean3 2.9671b 4.5000a 3.8362a

1 The position based on the tomato reference genome SL3.0 version2 The disease severity rating (DSR) to assessment the phenotype of late blight disease on tomato plants3 Means followed by the same letter are not significantly different at P < 0.05 (LSD test).

The superscripts of "a", "b", and "c" are alphabetical codes indicating significant differences when the letters are different.

https://doi.org/10.1371/journal.pone.0189951.t001



https://doi.org/10.1371/journal.pone.0189951.t001


markers in the candidate regions. Three types of plant materials are potentially used for the

validation: 1) an additional biparental population derived from the same crossing in the

genetic analysis (as in this study); 2) near-isogenic lines (NILs) having target loci of the donor

(e.g., a wild relative) with genetic background of the recurrent line (e.g., a cultivated line); and

3) a group of genetically divergent lines like natural populations or core collections maintain-

ing genetic diversity of genetic pools. Among them, NILs would be the most useful materials

to investigate the effects of the candidate locus on the phenotypes, and to identify the genes

controlling the phenotypes by a map-based cloning strategy. However, it would take a long

time and labors to develop NILs because of recurrent backcrossings with marker-assisted

selection. In Tomato Genetic Resource Center, University of California, Davis, series of NILs

covering the entire genome of LA1777 in the background of S. lycopersicum E6203 have been

registered [59]; however, NILs for chromosome 6 is not available at the time of writing unfor-

tunately. On the other hand, although a group of genetically divergent lines could be useful for

the validation, no resistance lines against P. infestans EG_12 have identified except for S. hab-rochaites LA1777 [15]. This meant that this approach might be not suitable for the case of this

study.

It should be possible to breed new varieties with high disease resistance by combining the

new locus with previously reported genes [19, 20]. Such a ‘gene pyramid’ strategy resulting in

durable resistance could contribute to successful management of new populations of P. infes-tans, which are resistant not only to well-known R genes, but also to certified fungicides, e.g.,

metalaxyl [60, 61]. Because we have characterized many P. infestans isolates [15, 62], as well as

tomato wild relatives highly resistant to these isolates [15], further novel resistance loci could

be identified from these materials using an approach similar to the one employed in this study.

The genotyping analysis was completed in a short time by taking advantage of two NGS

technologies, ddRAD-Seq and WGRS. In the former type of analysis, the number of detectable

SNPs depends on genetic diversity (i.e., the so-called genetic distance) of the materials [32, 63,

64]. In this study, because the parental lines were genetically divergent, the number of obtained

SNPs was 6,514. This result is consistent with a previous report in which 8,784 SNPs were

obtained from an interspecific cross between different species [65]. In intercrossing, or cross-

ing between closely related species, even though the number of SNPs obtained by ddRAD-Seq

might be small [66], WGRS has the potential to overcome this issue [43]. Therefore, lab work

is no longer a limiting factor in the discovery of new genetic loci.

ddRAD-Seq analysis and WGRS are powerful tools for gene mapping. Previously, it was

common to employ SSR and SNP markers for such analysis [28, 29, 67]. However, because

these methods are time-consuming and laborious, it used to be difficult to analyze multiple

populations at once. Furthermore, even if genetic loci could be narrowed down to small geno-

mic regions, subsequent sequencing of the target regions was necessary for identification of

candidate genes of interest. By contrast, ddRAD-Seq analysis can be performed in parallel

across multiple mapping populations. In addition, WGRS is the most effective and easiest

method for identifying sequence variations in candidate regions. In this study, the alignment

rate of the sequence reads to reference sequence was lower in LA1777 than in Castlerock, likely

because LA1777 is a wild species belonging to the Eriopersicon subsection, which is distantly

associated with cultivated lines such as Castlerock and Heinz 1706 [10].

The distribution patterns of SNPs over the genome was highly biased, with higher density

at the distal ends of chromosomes and lower density in pericentromeric regions. This observa-

tion was consistent with some previous studies [6, 29, 66] but discordant with another [10].

On the other hand, the density of SNPs identified by the WGRS in this study (512.8 SNPs per

100 kb) was higher than that in a previous study using only cultivated lines (11.9–98.9 SNPs

per 100 kb) [33], confirming that wild tomato relatives are genetically distant from cultivated




tomato. Thus, it is possible for the WGRS technology to dissect target quantitative traits at

nucleotide scale.

Furthermore, WGRS also makes it possible to predict the effects of sequence variations on

gene function, which facilitates the identification of candidate genes. In this study, we identi-

fied three candidate R genes encoding a nucleotide-binding site leucine-rich repeat

(NBS-LRR) protein, a mitogen-activated protein kinase kinase kinase (MAPKKK), and a

receptor-like protein kinase (RLK); these gene families are involved in disease resistance and

signaling pathways linked to plant innate immunity not only in tomato, but also in other plant

species [68–71]. Indeed, outside the candidate region, we identified moderate-impact SNPs in

genes encoding serine/threonine-protein kinases. These genes play important roles in disease

resistance and biological defense systems, inducing reactive oxygen species (ROS) bursts and

stimulating MAP kinases, as demonstrated in Arabidopsis [72]. Thus, these genes might confer

high disease resistance on LA1777. Furthermore, LA1777 possesses other R genes to many

types of biotic stresses [73, 74], because it has not undergone the domestication process, which

decreases the level of resistances [75]. The combination of ddRAD-Seq and WGRS could facili-

tate identification of genes of interest in LA1777. In addition to the genotyping methods, com-

parative genomics and transcriptomics in tomato and its relatives are useful methods in the

post–genome sequencing era [6, 39, 41, 42].

The resolution of genetic mapping depends on the frequency of chromosome recombina-

tion in the population, which unfortunately remains uncontrollable. Therefore, even though

ddRAD-Seq and WGRS are available, identification of target genes requires fine-mapping.

Accordingly, we performed additional DNA marker analysis with SSRs and/or SNPs in the tar-

get regions. In the future, due to decreasing sequencing costs for NGS analysis, it will become

feasible to perform WGRS across entire mapping populations, not only the parental lines,

potentially making fine-mapping with SSR markers and SNPs unnecessary. Disruption of gene

functions using genome-editing technologies is also an effective approach for elucidating the

functions of genes responsible for target traits.

In conclusion, using the ddRAD-Seq and WGRS NGS technologies, we identified a new resis-

tance locus for late blight disease caused by P. infestans. DNA markers linked to the locus could

be used in MAS in future breeding programs aimed at increasing resistance to this disease. In

addition, this approach provides a model for identifying not only additional R genes from tomato

relatives and P. infestans isolates, which our group identified in a previous study [15, 62], but also

other genes responsible for desirable agronomical traits. Furthermore, our results confirmed that,

as previously reported [15, 58], S. habrochaites accession LA1777 represents a useful genetic

resource for smart tomato breeding programs, genetics, and genomics studies.

Supporting information

S1 Fig. Physical positions of SNP markers across the tomato chromosomes (Chr00 –

Chr12) except ch06 using R package. The SNP markers were generated using the next-gener-

ation sequencing technologies and mapped on the reference genome of tomato SL3.0 version,

(A) the double-digest restriction site–associated DNA sequencing (ddRAD-Seq), (B) the

whole-genome shotgun resequencing (WGRS) approaches.

(PDF)

S1 Table. Sequences of oligonucleotides used in ddRAD-Seq.

(XLSX)

S2 Table. Information of TES and TGS-SSR markers used in the current study.

(XLSX)



http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0189951.s001




S3 Table. Number of paired-reads and alignment rate of ddRAD-Seq data for F2 popula-

tion mapped onto the tomato reference genome SL3.0.

(XLSX)

S4 Table. Distribution of the SNP markers on the 12 tomato chromosomes from WGRS

and ddRAD-Seq analysis.

(XLSX)

S5 Table. Number of paired-reads and alignment rate of cv. Castlerock and LA1777 gener-

ated from WGRS analysis mapped onto the tomato reference genome SL3.0 version.

(XLSX)

S6 Table. Putative impact of SNPs on gene functions in the tomato genome of WGRS and

candidate regions data.

(XLSX)

Acknowledgments

We are grateful to S. Sasamoto, C. Mimani, and H. Tsuruoka at the Kazusa DNA Research

Institute for their technical assistance. The authors would also like to thank the Tomato

Genetic Resources Center, USA, for providing us with the tomato wild accession used in this

study. Also, we want to thank prof. Elmahdy Metwally for his help for crossing and develop

the F2 seeds.

Author Contributions

Conceptualization: Ramadan A. Arafa, Mohamed T. Rakha.

Data curation: Ramadan A. Arafa, Kenta Shirasawa.

Formal analysis: Ramadan A. Arafa, Kenta Shirasawa.

Investigation: Ramadan A. Arafa, Kenta Shirasawa.

Project administration: Mohamed T. Rakha.

Resources: Ramadan A. Arafa, Mohamed T. Rakha.

Supervision: Mohamed T. Rakha, Nour Elden K. Soliman, Olfat M. Moussa, Said M. Kamel,

Kenta Shirasawa.

Validation: Nour Elden K. Soliman, Olfat M. Moussa, Said M. Kamel, Kenta Shirasawa.

Visualization: Ramadan A. Arafa.

Writing – original draft: Ramadan A. Arafa, Kenta Shirasawa.

Writing – review & editing: Mohamed T. Rakha.

References1. Lukyanenko AN. Disease resistance in tomato. In: Kalloo G, editor. Genetic Improvement of Tomato.

Berlin Heidelberg: Springer-Verlag; 1991. pp. 99–119.

2. Fry W. Phytophthora infestans: the plant (and R gene) destroyer. Mol Plant Pathol. 2008; 9(3):385–402.

https://doi.org/10.1111/j.1364-3703.2007.00465.x PMID: 18705878

3. FAO Statistical Databases [Internet]. FAOSTAT: Food and agriculture organization of the United

Nations, Statistics Division—[cited 2017]. Available from: http://www.fao.org/faostat/en/#data/QC

4. Govers F. Late blight: the perspective from the pathogen. In: Haverkort AJ, Struik PC, editors. Potato in

progress: Science meets practice. Wageningen Netherland: Academic Publishers; 2005. pp. 245–54.







https://doi.org/10.1111/j.1364-3703.2007.00465.x

http://www.ncbi.nlm.nih.gov/pubmed/18705878

http://www.fao.org/faostat/en/#data/QC


5. Bernatzky R, Tanksley SD. Toward a saturated linkage map in tomato based on isozymes and random

cDNA sequences. Genetics. 1986 Apr 1; 112(4):887–98. PMID: 17246322

6. The Tomato Genome Consortium. The tomato genome sequence provides insights into fleshy fruit evo-

lution, Nature. 2012; 485, 635–41. https://doi.org/10.1038/nature11119 PMID: 22660326

7. Shirasawa K, Fukuoka H, Matsunaga H, Kobayashi Y, Kobayashi I, Hirakawa H, et al. Genome-wide

association studies using single nucleotide polymorphism markers developed by re-sequencing of the

genomes of cultivated tomato. DNA Res. 2013a; 20(6):593–603. https://doi.org/10.1093/dnares/dst033

PMID: 23903436

8. Kobayashi M, Nagasaki H, Garcia V, Just D, Bres C, Mauxion JP, et al. Genome-wide analysis of intra-

specific DNA polymorphism in ‘Micro-Tom’, a model cultivar of tomato (Solanum lycopersicum). Plant

Cell Physiol. 2014; 55(2):445–54. https://doi.org/10.1093/pcp/pct181 PMID: 24319074

9. Celik I, Gurbuz N, Uncu AT, Frary A, Doganlar S. Genome-wide SNP discovery and QTL mapping for

fruit quality traits in inbred backcross lines (IBLs) of solanum pimpinellifolium using genotyping by

sequencing. BMC Genomics. 2017; 18(1):1. https://doi.org/10.1186/s12864-016-3406-7 PMID:

28049423

10. Sahu KK, Chattopadhyay D. Genome-wide sequence variations between wild and cultivated tomato

species revisited by whole genome sequence mapping. BMC Genomics. 2017; 18(1):430. https://doi.

org/10.1186/s12864-017-3822-3 PMID: 28576139

11. Miller JC, Tanksley SD. RFLP analysis of phylogenetic relationships and genetic variation in the genus

Lycopersicon. TAG Theor Appl Genet. 1990; 80(4):437–48. https://doi.org/10.1007/BF00226743

PMID: 24221000

12. Firdaus S, van Heusden AW, Hidayati N, Supena ED, Visser RG, Vosman B. Resistance to Bemisia

tabaci in tomato wild relatives. Euphytica. 2012; 187(1):31–45.

13. Haggard JE, Johnson EB, Clair DA. Linkage relationships among multiple QTL for horticultural traits

and late blight (P. infestans) resistance on chromosome 5 introgressed from wild tomato Solanum hab-

rochaites. G3: Genes, Genomes, Genetics. 2013; 3(12):2131–46.

14. Haggard JE, Johnson EB, Clair DA. Multiple QTL for horticultural traits and quantitative resistance to

Phytophthora infestans linked on Solanum habrochaites chromosome 11. G3: Genes, Genomes,

Genetics. 2015; 5(2):219–33.

15. Arafa RA, Moussa OM, Soliman NE, Shirasawa K, Kamel SM, Rakha MT. Resistance to Phytophthora

infestans in tomato wild relatives. Afr. J. Agric. Res. 2017; 12(26):2188–96.

16. Gallegly ME, Marvel ME. Inheritance of resistance to tomato race 0 of Phytophthora infestans. Phytopa-

thology. 1955; 45:103–9.

17. Peirce LC. Linkage tests with Ph conditioning resistance to race 0, Phytophthora infestans. Rep.

Tomato Genet. Coop. 1971; 21:30.

18. Eshed Y, Zamir D. An introgression line population of Lycopersicon pennellii in the cultivated tomato

enables the identification and fine mapping of yield-associated QTL. Genetics. 1995; 141(3):1147–62.

PMID: 8582620

19. Kole C, Ashrafi H, Lin G, Foolad M. Identification and molecular mapping of a new R gene, Ph-4, confer-

ring resistance to late blight in tomato. Proceedings of Solanaceae Conference; 2006; University of Wis-

consin, Madison, Abstract 449.

20. Foolad MR, Merk HL, Ashrafi H. Genetics, genomics and breeding of late blight and early blight resis-

tance in tomato. Crit Rev Plant Sci. 2008; 27(2):75–107.

21. Robert VJ, West MA, Inai S, Caines A, Arntzen L, Smith JK, et al. Marker-assisted introgression of

blackmold resistance QTL alleles from wild Lycopersicon cheesmanii to cultivated tomato (L. esculen-

tum) and evaluation of QTL phenotypic effects. Mol Breed. 2001; 8(3):217–33.

22. Brouwer DJ, Jones ES, Clair DA. QTL analysis of quantitative resistance to Phytophthora infestans

(late blight) in tomato and comparisons with potato. Genome. 2004; 47(3):475–92. https://doi.org/10.

1139/g04-001 PMID: 15190365

23. Brouwer DJ, Clair DS. Fine mapping of three quantitative trait loci for late blight resistance in tomato

using near isogenic lines (NILs) and sub-NILs. Theor Appl Genet. 2004; 108(4):628–38. https://doi.org/

10.1007/s00122-003-1469-8 PMID: 14586504

24. Ashrafi H, Kinkade M, Foolad MR. A new genetic linkage map of tomato based on a Solanum lycopersi-

cum× S. pimpinellifolium RIL population displaying locations of candidate pathogen response genes.

Genome. 2009; 52(11):935–56. https://doi.org/10.1139/g09-065 PMID: 19935918

25. Shirasawa K, Hirakawa H. DNA marker applications to molecular genetics and genomics in tomato.

Breed Sci. 2013b; 63(1):21–30. https://doi.org/10.1270/jsbbs.63.21 PMID: 23641178

26. Vıquez-Zamora M, Vosman B, van de Geest H, Bovy A, Visser RG, Finkers R, et al. Tomato breeding in

the genomics era: insights from a SNP array. BMC Genomics. 2013; 14(1):354.




https://doi.org/10.1038/nature11119


https://doi.org/10.1093/dnares/dst033


https://doi.org/10.1093/pcp/pct181


https://doi.org/10.1186/s12864-016-3406-7


https://doi.org/10.1186/s12864-017-3822-3

https://doi.org/10.1186/s12864-017-3822-3


https://doi.org/10.1007/BF00226743



https://doi.org/10.1139/g04-001

https://doi.org/10.1139/g04-001


https://doi.org/10.1007/s00122-003-1469-8

https://doi.org/10.1007/s00122-003-1469-8


https://doi.org/10.1139/g09-065


https://doi.org/10.1270/jsbbs.63.21



27. Jimenez-Gomez JM, Alonso-Blanco C, Borja A, Anastasio G, Angosto T, Lozano R, et al. Quantitative

genetic analysis of flowering time in tomato. Genome. 2007; 50(3):303–15. https://doi.org/10.1139/g07-

009 PMID: 17502904

28. Ohyama A, Asamizu E, Negoro S, Miyatake K, Yamaguchi H, Tabata S, et al. Characterization of

tomato SSR markers developed using BAC-end and cDNA sequences from genome databases. Mol

Breed. 2009; 23(4):685–91.

29. Shirasawa K, Asamizu E, Fukuoka H, Ohyama A, Sato S, Nakamura Y, et al. An interspecific linkage

map of SSR and intronic polymorphism markers in tomato. Theor Appl Genet. 2010; 121(4):731–9.

https://doi.org/10.1007/s00122-010-1344-3 PMID: 20431859

30. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, et al. Rapid SNP discovery and

genetic mapping using sequenced RAD markers. PloS One. 2008; 3(10):e3376. https://doi.org/10.

1371/journal.pone.0003376 PMID: 18852878

31. Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH. Stacks: building and genotyping loci

de novo from short-read sequences. G3: Genes, genomes, genetics. 2011; 1(3):171–82.

32. Davey JW, Cezard T, Fuentes-Utrilla P, Eland C, Gharbi K, Blaxter ML. Special features of RAD

sequencing data: implications for genotyping. Mol Ecol. 2013; 22(11):3151–64. https://doi.org/10.1111/

mec.12084 PMID: 23110438

33. Shirasawa K, Hirakawa H, Isobe S. Analytical workflow of double-digest restriction site-associated DNA

sequencing based on empirical and in silico optimization in tomato. DNA Res. 2016a; 23(2):145–53.

https://doi.org/10.1093/dnares/dsw004 PMID: 26932983

34. Chutimanitsakun Y, Nipper RW, Cuesta-Marcos A, Cistue L, Corey A, Filichkina T, et al. Construction

and application for QTL analysis of a restriction site associated DNA (RAD) linkage map in barley. BMC

genomics. 2011; 12(1):4.

35. Pfender WF, Saha MC, Johnson EA, Slabaugh MB. Mapping with RAD (restriction-site associated

DNA) markers to rapidly identify QTL for stem rust resistance in Lolium perenne. Theor Appl Genet.

2011; 122(8):1467–80. https://doi.org/10.1007/s00122-011-1546-3 PMID: 21344184

36. Truong HT, Ramos AM, Yalcin F, de Ruiter M, van der Poel HJ, Huvenaars KH, et al. Sequence-based

genotyping for marker discovery and co-dominant scoring in germplasm and populations. PLoS One.

2012; 7(5):e37565. https://doi.org/10.1371/journal.pone.0037565 PMID: 22662172

37. Wang N, Fang L, Xin H, Wang L, Li S. Construction of a high-density genetic map for grape using next

generation restriction-site associated DNA sequencing. BMC Plant Biol. 2012; 12(1):148.

38. Etter PD, Bassham S, Hohenlohe PA, Johnson EA, Cresko WA. SNP discovery and genotyping for evo-

lutionary genetics using RAD sequencing. In: Orgogozo V, Rockman MV, editors. Molecular Methods

for Evolutionary Genetics, Methods in Molecular Biology. The Netherlands: Springer; 2011. pp. 157–

78.

39. Bolger A, Scossa F, Bolger ME, Lanz C, Maumus F, Tohge T, et al. The genome of the stress-tolerant

wild tomato species Solanum pennellii. Nature Genet. 2014; 46(9):1034–8. https://doi.org/10.1038/ng.

3046 PMID: 25064008

40. Aflitos S, Schijlen E, Jong H, Ridder D, Smit S, Finkers R, et al. Exploring genetic variation in the tomato

(Solanum section Lycopersicon) clade by whole-genome sequencing. Plant J. 2014; 80(1):136–48.

https://doi.org/10.1111/tpj.12616 PMID: 25039268

41. Causse M, Desplat N, Pascual L, Le Paslier MC, Sauvage C, Bauchet G, et al. Whole genome rese-

quencing in tomato reveals variation associated with introgression and breeding events. BMC Geno-

mics. 2013; 14(1):791.

42. Lin T, Zhu G, Zhang J, Xu X, Yu Q, Zheng Z, et al. Genomic analyses provide insights into the history of

tomato breeding. Nature Genet. 2014; 46(11):1220–6. https://doi.org/10.1038/ng.3117 PMID:

25305757

43. Shirasawa K, Kuwata C, Watanabe M, Fukami M, Hirakawa H, Isobe S. Target amplicon sequencing

for enotyping genome-wide single nucleotide polymorphisms identified by whole-genome resequencing

in Peanut. Plant Genome. 2016b; 9(3).

44. Caten CE, Jinks JL. Spontaneous variability of single isolates of Phytophthora infestans. I. Cultural vari-

ation. Can J Bot. 1968; 46(4):329–48.

45. IvanovićM, MijatovićM, Zečević B, Niepold F. Occurrence of New Populations and Mating Types of

Phytophthora infestans (Mont) de Bary in Serbia. Acta Hortic. 2004; 729: 499–502.

46. Dorrance AE, Inglis DA. Assessment of greenhouse and laboratory screening methods for evaluating

potato foliage for resistance to late blight. Plant Dis. 1997; 81(10):1206–13.

47. Chunwongse J, Chunwongse C, Black L, Hanson P. Molecular mapping of the Ph-3 gene for late blight

resistance in tomato. J Hortic Sci Biotechnol. 2002; 77(3):281–6.



https://doi.org/10.1139/g07-009

https://doi.org/10.1139/g07-009


https://doi.org/10.1007/s00122-010-1344-3





https://doi.org/10.1111/mec.12084

https://doi.org/10.1111/mec.12084


https://doi.org/10.1093/dnares/dsw004


https://doi.org/10.1007/s00122-011-1546-3




https://doi.org/10.1038/ng.3046



https://doi.org/10.1111/tpj.12616





48. Shirasawa K, Hirakawa H, Nunome T, Tabata S, Isobe S. Genome-wide survey of artificial mutations

induced by ethyl methanesulfonate and gamma rays in tomato. Plant Biotechnol J. 2016c; 14(1):51–60.

https://doi.org/10.1111/pbi.12348 PMID: 25689669

49. Shirasawa K, Tanaka M, Takahata Y, Ma D, Cao Q, Liu Q, et al. A high-density SNP genetic map con-

sisting of a complete set of homologous groups in autohexaploid sweetpotato (Ipomoea batatas). Sci

Rep. 2017;7.

50. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics.

2011; 27(6):863–4. https://doi.org/10.1093/bioinformatics/btr026 PMID: 21278185

51. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012; 9(4):357–9.

https://doi.org/10.1038/nmeth.1923 PMID: 22388286

52. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format

and SAMtools. Bioinformatics. 2009; 25(16):2078–9. https://doi.org/10.1093/bioinformatics/btp352

PMID: 19505943

53. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and

VCFtools. Bioinformatics. 2011; 27(15):2156–8. https://doi.org/10.1093/bioinformatics/btr330 PMID:

21653522

54. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predict-

ing the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melano-

gaster strain w1118; iso-2; iso-3. Fly. 2012; 6(2):80–92. https://doi.org/10.4161/fly.19695 PMID:

22728672

55. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for

association mapping of complex traits in diverse samples. Bioinformatics. 2007; 23(19):2633–5. https://

doi.org/10.1093/bioinformatics/btm308 PMID: 17586829

56. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to mul-

tiple testing. J R Stat Soc Series B Stat Methodol. 1995:289–300.

57. Smart CD, Tanksley SD, Mayton H, Fry WE. Resistance to Phytophthora infestans in Lycopersicon

pennellii. Plant Dis. 2007; 91(8):1045–9.

58. Li J, Liu L, Bai Y, Finkers R, Wang F, Du Y, et al. Identification and mapping of quantitative resistance to

late blight (Phytophthora infestans) in Solanum habrochaites LA1777. Euphytica. 2011; 179(3):427–38.

59. Monforte AJ, Tanksley SD. Development of a set of near isogenic and backcross recombinant inbred

lines containing most of the Lycopersicon hirsutum genome in a L. esculentum genetic background: a

tool for gene mapping and gene discovery. Genome 2000; 43(5):803–813. PMID: 11081970

60. Saville A, Graham K, Grunwald NJ, Myers K, Fry WE, Ristaino JB. Fungicide sensitivity of US geno-

types of Phytophthora infestans to six oomycete-targeted compounds. Plant Dis. 2015; 99(5):659–66.

61. Montes MS, Nielsen BJ, Schmidt SG, Bødker L, Kjøller R, Rosendahl S. Population genetics of Phy-

tophthora infestans in Denmark reveals dominantly clonal populations and specific alleles linked to

metalaxyl-M resistance. Plant Pathol. 2016; 65(5):744–53.

62. Arafa RA, Soliman NEK, Moussa OM, Kamel SM, Shirasawa K. Characterization of Egyptian Phy-

tophthora infestans population using simple sequence repeat markers. J. Gen. Plant Pathol.

Forthcoming.

63. Zhao Y, Gowda M, Liu W, Wurschum T, Maurer HP, Longin FH, et al. Accuracy of genomic selection in

European maize elite breeding populations. Theor Appl Genet. 2012; 124(4):769–76. https://doi.org/10.

1007/s00122-011-1745-y PMID: 22075809

64. Yamamoto E, Matsunaga H, Onogi A, Kajiya-Kanegae H, Minamikawa M, Suzuki A, et al. A simulation-

based breeding design that uses whole-genome prediction in tomato. Sci. Rep. 2016; 6:19454. https://

doi.org/10.1038/srep19454 PMID: 26787426

65. Sim SC, Durstewitz G, Plieske J, Wieseke R, Ganal MW, Van Deynze A, et al. Development of a large

SNP genotyping array and generation of high-density genetic maps in tomato. PLoS One. 2012; 7(7):

e40563. https://doi.org/10.1371/journal.pone.0040563 PMID: 22802968

66. Chen AL, Liu CY, Chen CH, Wang JF, Liao YC, Chang CH, et al. Reassessment of QTLs for late blight

resistance in the tomato accession L3708 using a restriction site associated DNA (RAD) linkage map

and highly aggressive isolates of Phytophthora infestans. PloS One. 2014; 9(5):e96417. https://doi.org/

10.1371/journal.pone.0096417 PMID: 24788810

67. Sim SC, Van Deynze A, Stoffel K, Douches DS, Zarka D, Ganal MW, et al. High-density SNP genotyp-

ing of tomato (Solanum lycopersicum L.) reveals patterns of genetic variation due to breeding. PloS

One. 2012; 7(9):e45520. https://doi.org/10.1371/journal.pone.0045520 PMID: 23029069

68. Melech-Bonfil S, Sessa G. Tomato MAPKKKε is a positive regulator of cell-death signaling networks

associated with plant immunity. Plant J. 2010; 64(3):379–91. PMID: 21049563



https://doi.org/10.1111/pbi.12348


https://doi.org/10.1093/bioinformatics/btr026


https://doi.org/10.1038/nmeth.1923


https://doi.org/10.1093/bioinformatics/btp352


https://doi.org/10.1093/bioinformatics/btr330


https://doi.org/10.4161/fly.19695


https://doi.org/10.1093/bioinformatics/btm308

https://doi.org/10.1093/bioinformatics/btm308



https://doi.org/10.1007/s00122-011-1745-y

https://doi.org/10.1007/s00122-011-1745-y


https://doi.org/10.1038/srep19454

https://doi.org/10.1038/srep19454











69. Mace E, Tai S, Innes D, Godwin I, Hu W, Campbell B, et al. The plasticity of NBS resistance genes in

sorghum is driven by multiple evolutionary processes. BMC Plant Biol. 2014; 14(1):253.

70. Devran Z, Kahveci E, Ozkaynak E, Studholme DJ, Tor M. Development of molecular markers tightly

linked to Pvr4 gene in pepper using next-generation sequencing. Mol Breed. 2015; 35(4):101. https://

doi.org/10.1007/s11032-015-0294-5 PMID: 25798050

71. Li Y, Ruperao P, Batley J, Edwards D, Davidson J, Hobson K, et al. Genome analysis identified novel

candidate genes for ascochyta blight resistance in chickpea using whole genome re-sequencing data.

Front Plant Sci. 2017;8.

72. Lin ZJ, Liebrand TW, Yadeta KA, Coaker GL. PBL13 is a serine/threonine protein kinase that negatively

regulates Arabidopsis immune responses. Plant Physiol. 2015: 2950–62. https://doi.org/10.1104/pp.15.

01391 PMID: 26432875

73. Al Abdallat AM, Al Debei HS, Asmar H, Misbeh S, Quraan A, Kvarnheden A. An efficient in vitro-inocula-

tion method for Tomato yellow leaf curl virus. Virol. J. 2010; 7(1):84.

74. Momotaz A, Scott JW, Schuster DJ. Identification of quantitative trait loci conferring resistance to Bemi-

sia tabaci in an F2 population of Solanum lycopersicum× Solanum habrochaites accession LA1777. J

Am Soc Hortic Sci. 2010; 135(2):134–42.

75. Bergougnoux V. The history of tomato: from domestication to biopharming. Biotechnol Adv. 2014; 32

(1):170–89. https://doi.org/10.1016/j.biotechadv.2013.11.003 PMID: 24211472



https://doi.org/10.1007/s11032-015-0294-5

https://doi.org/10.1007/s11032-015-0294-5


https://doi.org/10.1104/pp.15.01391

https://doi.org/10.1104/pp.15.01391


https://doi.org/10.1016/j.biotechadv.2013.11.003



Rapid identification of candidate genes for resistance to ...203.64.245.61/full_text/e13786.pdf · control (cv. Castlerock), were sown in 209 cell seedling trays with peat moss–vermiculi

Documents